本研究嘗試不全以負面假設來看待配戴眼鏡對人臉辨識的影響。吾人將以邊緣偵測圖為基礎，以邊點強度的分析來建立一套定位眼鏡的偵測系統。同時用偵測出的鏡框位置，以邊緣點的強度、密度比較的方式，定位眼睛的位置；並以前述兩套偵測演算，採擷其過程的資訊，進一步地定位鼻子與嘴巴的位置。這些演算形成一個簡易的人臉特徵定位系統，其將可處理配戴眼鏡的人臉；吾人也將進一步地經由其處理過程與結果，分析眼鏡對區域人臉辨識的影響，進而引導出非自然物件可能對人臉辨識的阻礙或輔助。 論文也將以全域比對法中的PCA與ICA演算法作一連串的實驗，剖析眼鏡對於全域辨識的影響；此外，亦用相同的方法來測試非自然物(眼鏡)、光源亮度與人臉角度對於人臉辨識阻礙的程度，以探究是否系統值得花費更大的代價，來移除眼鏡這個被一致認定的人臉辨識障礙，並得以在辨識演算法上獲得更高的效能。 / The objective of this thesis is to investigate the efficacy of face recognition systems when the subjects are wearing glasses. We do not presume that non-facial features such as glasses are nuisances. Instead, we will study whether the inclusion of glasses will have a positive impact on the face detection procedure and how it affects the feature extraction process. We will demonstrate how to use techniques based on local feature analysis to reduce the uncertainties in the matching result due to interferences around the eyes and nose caused by optical glasses. We have also conducted extensive experiments to analyze the effect of glasses on face recognition systems based on global matching strategy. Specifically, we perform both principal component analysis (PCA) and independent component analysis (ICA) on face databases with different percentage of subjects wearing eye glasses. It is concluded that external objects such as glasses will have a negative impact on face recognition using global analysis approaches. However, the adverse influences of illumination and pose are more conspicuous during the recognition process. Therefore, one should take caution when attempting to adapt the global matching scheme to handle the difficulties caused by glasses.

地面光達系統可以快速獲取大量且高精度之點雲資料，這些點雲資料不但記錄了被掃描物體之三維資訊，還包含其色彩訊息。但因光達點雲資料量過於龐大，若要直接於電腦上展示其三維模型，必須配合有效的資料處理技術，才能迅速且即時地將資料顯示於螢幕上。 我們針對地面光達系統獲取之建物點雲，提出一套處理方法，期盼透過少數關鍵點雲，就足以表示整個建物的模型。研究流程主要分為三階段，首先採用三維網格資料結構，從地面光達系統獲取之建物點雲中，萃取出關鍵點雲，並利用三維不規則三角網建模方式，進行模型建構工作，產生建物大略模型。其次再逐點判斷是否將剩餘之點加入此模型中，持續更新模型細微之部分。最後將點雲中的色彩資訊轉成影像，敷貼在模型表面上，讓整個模型更為逼真。 我們以政大綜合大樓進行實驗，成功地減少大量冗餘的點雲資料，只需要約原始點雲的1％，就足以將綜合大樓模型建構完成。為了達到可以從不同視角即時瀏覽建物模型，我們採用虛擬實境語言(VRML)來描述處理後的三維模型，遠端使用者只需透過一般網頁瀏覽器，即可即時顯示處理過的三維建物模型。 / Ground-based LIDAR system can be used to detect the surface of the buildings on the earth. In general, it produces large amount of high-precision point cloud data. These data include not only the three-dimensional space information, but also the color information. However, the number of point cloud data is huge and is difficult to be displayed efficiently. It’s necessary to use efficient data processing techniques in order to display these point cloud data in real-time. In this research, we construct the three-dimensional building model using the key points selected from a given set of point cloud data. The major works of our scheme consists of three parts. In the first part, we extract the key points from the given point cloud data through the help of a three-dimensional grid. These key points are used to construct a primitive model of the building. Then, we checked all the remaining points and decided whether these points are essential to the final building model. Finally, we transformed the color information into images and then used the transformed images to represent generic surface material of the three-dimensional model of the building. The goal of the final step is to make the model more realistic. In the experiments, we used the twin-tower of our university as our target. We successfully reduced the required data in displaying the building model and only about one percent of the original point cloud data are used in the final model. Hence, one can see the twin-tower from various view points in real-time. In addition, we use VRML to describe our model and the users can browse the results in real-time on internet.